Methods and systems for generating a horizon for use in an advanced driver assistance system (ADAS)

ABSTRACT

A method of generating a horizon for use by an ADAS of a vehicle involves using historical vehicle probe data to determine the likelihood that different outgoing paths are taken at a decision point along a currently traversed road segment, and deriving a probability that each path may be taken. The probability is based on historical paths taken by vehicles at the decision point as indicated by the vehicle probe data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of United States Utilitypatent application Ser. No. 13/378,717, which is the national stage ofInternational Patent Application No. PCT/US2010/035694 filed May 21,2010, and which claims the benefit of: U.S. Provisional PatentApplication No. 61/187,494 filed Jun. 16, 2009; U.S. Provisional PatentApplication No. 61/273,185 filed Aug. 3, 2009; and U.S. ProvisionalPatent Application No. 61/279,981 filed Oct. 29, 2009. The entirecontent of all these applications is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to methods and systems for generating ahorizon for use in an Advanced Driver Assistance System (ADAS) of avehicle.

BACKGROUND TO THE INVENTION

Advanced Driver Assistance Systems are increasingly used in vehicles toprovide assistance to drivers in areas such as braking, collisionavoidance, and speed selection. Such systems may help to reduce driverworkload, and may be useful in improving safety, vehicle operatingefficiency, driver comfort and/or fuel efficiency.

Operation of an ADAS relies upon knowledge of the road ahead, and itsproperties. For example, the ADAS may take into account factors such asthe curvature or gradient of a section of the road ahead in order todetermine a suitable speed for traversing the section, and may then, forexample, control the braking subsystems of the vehicle in order toimplement the determined speed. Typically a subsystem of the ADAS, whichmay be known as an ADAS horizon provider subsystem, communicates withADAS applications of a vehicle network over a vehicle bus, such as aController Area Network (CAN) bus, in order to control vehiclesubsystems. Different ADAS applications may control different respectivevehicle subsystems in accordance with the information received from theADAS horizon provider over the vehicle bus. For example, there may beADAS applications in respect of braking, suspension, etc. The ADAShorizon provider subsystem provides ADAS horizon information which maybe used by the ADAS applications associated with given vehiclesubsystems to provide control of the respective vehicle subsystems usingthe ADAS horizon data.

One aspect of the operation of the ADAS horizon provider subsystemrelates to the generation of a suitable ADAS “horizon” for communicationover the vehicle bus to the vehicle subsystems. The ADAS horizoncomprises digital map information about a portion of the road networkahead, which is used by the ADAS applications to implement ADASfunctionality with respect to the vehicle subsystems. Determination ofthe ADAS horizon involves predicting the path or paths that the vehiclemay travel in the immediate future, to ensure that the necessary data istransmitted over the vehicle bus to allow implementation of ADASfunctions by the vehicle subsystems as the vehicle travels.

The ADAS horizon may include information about the course of a roadahead, and associated attributes of the road, such as curvature,gradient, etc which may be used by ADAS applications of the vehicle toimplement ADAS control of the vehicle subsystems. ADAS applicationsassociated with different vehicle systems may filter the provided ADAShorizon data to extract the information required for controlling theirrelevant subsystem. For example, road curvature data may be extractedfor use in controlling the braking system.

When determining a suitable portion of the road network ahead forinclusion in the ADAS horizon, it is necessary to balance providingsufficient data to ensure that ADAS functionality may be adequatelyimplemented by vehicle systems while avoiding overloading the vehicleADAS applications associated with the vehicle systems. The prediction ofthe path or paths that the vehicle may be expected to travel in the nearfuture is therefore fundamental to the generation of a suitable ADAShorizon. The determination of a suitable ADAS horizon may involvecertain challenges, for example depending upon whether the vehicle isfollowing a pre-calculated route or not, and to accommodate potentialdeviations of a vehicle from a pre-calculated route. For example, in asimple case, the ADAS horizon may comprise data relating only to theroad currently being traversed up to a predetermined distance from acurrent position. However, in such situations, the ADAS applications maybe left “blind” for a time if the driver deviates from the roadcurrently being traversed until a new ADAS horizon can be generated inrelation to the newly traversed road section.

The Applicant has realised that there is a need for improved methods andsystems for generating a horizon for use by an ADAS, and in particular,for predicting a path or paths that a vehicle may travel in theimmediate future when generating an ADAS horizon.

SUMMARY OF THE INVENTION

In accordance with a first aspect of the invention there is provided amethod of generating a horizon for use in an ADAS of a vehicle,comprising:

-   -   generating the horizon using positional data relating to the        movement of a plurality of devices associated with vehicles with        respect to time.

In accordance with a second aspect of the invention there is provided asystem for generating a horizon for use in an ADAS of a vehicle, thesystem comprising:

-   -   means for generating the horizon using positional data relating        to the movement of a plurality of devices associated with        vehicles with respect to time.

The present invention in this further aspect may include any or all ofthe features described in relation to the first aspect of the invention,and vice versa, to the extent that they are not mutually inconsistent.Thus, if not explicitly stated herein, the system of the presentinvention may comprise means for carrying out any of the steps of themethod described.

The means for carrying out any of the steps of the method may comprise aset of one or more processors configured, e.g. programmed, for doing so.A given step may be carried out using the same or a different set ofprocessors to any other step. Any given step may be carried out using acombination of sets of processors. The system may further comprise datastorage means, such as computer memory, for storing, for example, thedata indicative of the generated horizon, and/or the data used indetermining the horizon. The means for generating the horizon may be ahorizon generating subsystem of an ADAS system.

As used herein, the term “horizon” refers to a driving horizon for useby an ADAS of a vehicle. The horizon includes a prediction of one ormore paths that the vehicle may travel in the immediate future through aportion of a road network or data enabling such a prediction to be made.The road network comprises a plurality of road segments connected bynodes and is represented by digital map data indicative of each roadsegment and node, i.e. decision point. In preferred embodiments thehorizon comprises data indicative of the relative probability that eachof a plurality of paths may be taken by the vehicle at a decision point.The horizon may comprise digital map data indicative of the or eachpredicted path and/or data indicative of one or more attributes of theor each predicted path. This may allow data about an upcoming portion ofthe road network to be obtained in advance before the vehicle reachesthe relevant portion, to enable the ADAS to function. The portion of theroad network may be defined by a boundary of the horizon.

By using positional data relating to the movement of devices associatedwith vehicles with respect to time in generating the horizon, it hasbeen found that a useful horizon containing data necessary forappropriate guidance of the vehicle may be obtained. In particular, theuse of such data has been found to allow the relative probability thateach of a plurality of possible paths may be taken at a decision pointto be more reliably determined. This may allow more accurate predictionof paths that may be taken by the vehicle to be made, resulting in morereliable operation of an ADAS system of the vehicle based on the ADAShorizon, even where the vehicle diverges from an expected most probablepath. By using data relating to the previous movement of actualvehicles, and which may, in some preferred embodiments at least, bespecific to a given time period and/or vehicle type, the probabilitythat a given path will be taken may be established with greaterconfidence than by relying solely on theoretical determinations, e.g.based on digital map data relating to the attributes of road segmentsdefining the paths. For example, such data may not be complete, or maynot reflect the significance of given paths in reality.

The positional data used in generating the horizon is data relating tothe movement of a plurality of devices associated with vehicles withrespect to time. Such data may be referred to as vehicle “probe data”,and any references to vehicle “probe data” herein, should be understoodto refer to such positional data. As the devices are associated withrespective vehicles, the position of the devices may be considered tocorrespond to the position of the vehicles. For brevity, the positionaldata relating to the movement of a plurality of devices associated withvehicles with respect to time may be referred to as the “positionaldata” herein.

The data that is used in generating the horizon is preferably obtainedover a long period of time, e.g. weeks, months, etc (i.e. can bereferred to as “historical data”). The positional data is preferablyused in determining the relative probabilities associated with takingeach possible outgoing path at a decision point as described in moredetail below, but more generally may be used in predicting one or morepossible paths in the horizon.

The generated horizon may extend from a current position of a vehicle toa predetermined distance ahead of the current position defining aboundary of the horizon. The ADAS horizon may extend to thepredetermined distance from the current vehicle position along the oreach predicted path. The distance may be up to 500 m, or up to 200 mand/or at least 100 m. A given distance ahead of a current positionrefers to a distance in the current direction of travel. The extent ofthe horizon may be chosen as desired for a given application. Thehorizon may extend to a given radius corresponding to the distance inthe direction of travel, e.g. through a 180 degree angle in the forwarddirection of travel.

As used herein, a “path” may comprise at least a portion of one or moreroad segments. A path is representative of a trajectory that may betaken by a vehicle through the road network. A path is a path defined byat least a portion of one or more road segments of a digital map. Thedigital map comprises a plurality of segments representative of roadsegments of a road network.

In accordance with the invention, the step of generating the horizoncomprises predicting one or more paths that may be taken by the vehicleusing the positional data. In preferred embodiments, the step ofgenerating the horizon comprises determining data indicative of therelative probability that each of a plurality of possible outgoing pathsassociated with a decision point will be taken by the vehicle in theimmediate future using the positional data.

It will be appreciated that the methods described herein in relation toa given decision point may be carried out in respect of any additionaldecision points of the road network as desired.

As used herein, the “relative probability” of a path of a set of aplurality of possible outgoing paths at a decision point being taken bya vehicle in the immediate future refers to the probability that thevehicle may be expected to take the path relative to the probabilitythat the vehicle will take any of the other paths of the set of aplurality of possible outgoing paths at the decision point in theimmediate future. References to a probable path or any other referenceto probability or likelihood of a path should be understood to refer tothe probability of the vehicle travelling along the path in theimmediate future.

Preferably, for each respective possible outgoing path at the decisionpoint, data is determined indicative of a relative probability that thepath will be taken in preference to any other one of the possibleoutgoing paths. The method may comprise associating the relativeprobability data with data indicative of the path to which it relatesand/or the decision point to which it relates.

It will be appreciated that the definition of an “outgoing” path, andindeed the relative probability that a given path will be taken, willdepend upon the incoming path to the decision point. Accordingly therelative probability that each of the plurality of possible outgoingpaths is taken is by reference to a given incoming path.

The method may comprise the step of determining an incoming path to thedecision point with respect to which the outgoing paths are to bedefined. The incoming path is a path along which the vehicle is expectedto travel to reach the decision point. In preferred embodiments theincoming path is a continuation of a road segment along which thevehicle is currently travelling. Alternatively or additionally the pathmay be a portion of a known most probable path for the vehicle, such asa portion of a pre-calculated route. In these cases the portion of theknown path is preferably an end portion of the known path whichterminates at the decision point. However, these preferred embodimentsof the invention may still be applied to determining the relativeprobability that each of a plurality of other paths may be taken at adecision point even where an outgoing path at the decision pointcorresponding to a portion of a pre-calculated route is known.

The decision point may be any decision point defining a plurality ofpossible outgoing paths for which it is desired to determine a relativeprobability associated with taking each of the possible paths. Inembodiments the decision point is the next decision point to beencountered by the vehicle along a continuation of a currently traversedroad segment. It may be assumed that the vehicle will continue along thecurrently traversed road segment at least until the next decision pointis reached. Thus the path as far as the next decision point may beconsidered to be known.

In some embodiments the method comprises identifying a current locationof the vehicle, determining a road segment along which the vehicle iscurrently travelling, and identifying the next decision point to beencountered. The method may then comprise determining the relativeprobability that each of a plurality of outgoing paths associated withthe decision point will be taken in accordance with the methods of theinvention.

It is envisaged that the preferred methods of determining the relativeprobabilities associated with paths at a decision point may be carriedout “on the fly”. Thus, preferably the decision point is an upcomingdecision point or next decision point to be encountered. Nonetheless, itis envisaged that the method could be carried out with respect to anydecision point of a road network, or could be applied to determiningrelative probability values in advance that could be stored inassociation with data identifying each decision point to which theyrelate in a database or similar for subsequent use as desired. In thiscase, the incoming path with respect to which the outgoing path(s) aredefined may be arbitrarily chosen, and data may be obtained for a givendecision point in respect of multiple possible incoming paths.

In any of its embodiments, the method may comprise selecting a decisionpoint, and determining an incoming path and a plurality of outgoingpaths associated with the decision point for which relativeprobabilities are to be determined.

The decision point may be any type of decision point at which two ormore possible outgoing paths exist for a given incoming path. Thedecision point may be any form of intersection, roundabout, junction,crossing, divergence of a path, etc.

The method preferably involves determining data indicative of a relativeprobability that each of a set of two or more possible outgoing pathswill be taken by the vehicle at the decision point (for a given incomingpath). Preferably the method comprises determining relative probabilitydata in respect of every possible outgoing path present at the decisionpoint in respect of the given incoming path. An outgoing path may bedefined as any path originating from the decision point other than theincoming path. The possible outgoing paths may or may not include allpotential outgoing paths associated with the decision point, and certainpaths may be excluded from consideration for various reasons e.g. asthey are considered to be in a direction close to that opposite to thedirection of travel, are below a significance threshold, etc. Forexample, the path corresponding to the incoming path but in the oppositetravel direction may not be considered for a junction, but may beconsidered for a roundabout. Such paths that are not considered are notdeemed to be “possible” outgoing paths. Unless the context demandsotherwise, references herein to an “outgoing path” should be understoodto refer to a “possible outgoing path”. The methods of the presentinvention are therefore carried out with respect to a set of a pluralityof possible outgoing paths at the decision point. The set of theplurality of possible outgoing paths are those paths for which relativeprobability data is desired to be determined, i.e. which paths areconsidered relevant paths for a given application.

In some embodiments in which one of the possible outgoing paths at thedecision point is known to correspond to a portion of a pre-calculatedroute, the method may comprise excluding that outgoing path from the setof plurality of outgoing paths whose relative probabilities aredetermined, or adjusting the calculations appropriately to ensure thatthis route is determined to be the most probable. This may be done byassigning the path corresponding to the route a probability of one and,for example, adjusting the probabilities of the other paths accordingly,or by adjusting the relative probabilities of the other paths such thatnone is higher than that of the path corresponding to the route.

The step of determining the data indicative of a relative probabilitythat each possible outgoing path of a plurality of paths may be taken bythe vehicle may comprise ranking each path according to the likelihoodthat the vehicle may be expected to travel along the path in preferenceto any other one of the paths of the set of a plurality of possibleoutgoing paths. Thus, the relative probability may be in terms of aqualitative ordering of the paths. In other embodiments the step maycomprise determining a probability factor in respect of each possibleoutgoing path indicative of the relative probability that the path willbe taken in preference to any other one of the paths. The probabilityfactor provides a quantitative measure of the relative probability thatthe path will be taken.

The step of determining the data indicative of the relative probabilitythat a given possible outgoing path may be taken is carried out usingthe positional data, e.g. historical vehicle probe data.

The step of determining the relative probability of a given possibleoutgoing path may further comprise using data indicative of the incomingpath that the vehicle is expected to travel to reach the decision point.

The method may comprise storing the determined data indicative of therelative probability that each possible outgoing path will be taken. Thestored data may be indicative of a rank or probability factor for thepath. The method may comprise storing data indicative of a relativeprobability that the path will be taken in preference to any other oneof the paths for each possible outgoing path in association with dataidentifying the path. The method may further comprise storing dataindicative of the incoming path with respect to which the outgoing pathsare defined. The method may comprise storing the data indicative of therelative probability of a possible outgoing path being taken inassociation with data indicative of the decision point to which itrelates, e.g. the location of the decision point. The location of thedecision point may be in absolute terms or in relation to a distancealong an, for example, most probable path, etc.

Some preferred embodiments of the invention will now be describedillustrating the way in which the positional data may be used indetermining the data indicative of the relative probability of eachpossible outgoing path being taken. It will be appreciated that theprobability of a path being taken will be with respect to a givenincoming path as described above. Other types of data may additionallybe used. For example, digital map data, vehicle data and/or driver datamay additionally be used in combination with the positional data. Asuitable probability function indicative of the relative probability ofa path may be constructed to take account of any or all of thesefactors, and with an appropriate weight assigned to each as desired.

The method may comprise determining the relative probability data usingdata indicative of a historic relative probability that each of theplurality of possible outgoing paths from the decision point has beentaken (in respect of the incoming path) based on the positional datai.e. historical probe data. The method may comprise associating arelatively higher probability with a possible outgoing path that isassociated with a relatively higher probability of having been selectedbased on the historic probability data. The relative probability thatthe paths were chosen historically may be used alone, or as a weightingfactor together with other factors to determine the relative probabilitythat paths will be chosen.

The method may extend to determining the data indicative of the historicrelative probability that each of the plurality of possible outgoingpaths has been taken for the given incoming path. This may be carriedout using the (historical) positional data relating to the movement of aplurality of devices associated with vehicles with respect to time in aportion of a road network comprising the decision point. The method maycomprise using the positional data to determine a relative frequencywith which vehicles have taken each of the plurality of possibleoutgoing paths from the decision point in respect of the incoming path.The historical probability data may be obtained using a count indicativeof the number of times that each path is taken. In other embodiments themethod may comprise obtaining the historic relative probability datafrom a database comprising data indicative of the frequency with whicheach of a plurality of possible outgoing paths has been taken at one ormore, and preferably a plurality of, or each, decision points of a roadnetwork for one or more, and preferably a plurality of, or each,possible incoming path of the or each decision point.

The historical probability may be dependent upon time and/or vehicletype. Thus, multiple historical probabilities may be determined for agiven outgoing path at a junction in respect of different time periods.The time periods may be times of day and/or week. For example, a countindicative of the number of times that a given path is taken by a deviceassociated with a vehicle in a given time frame may be determined andused in determining the historic probability for the path that isapplicable for the relevant time frame.

Alternatively or additionally, a count may be determined in respect ofdifferent types of vehicle, e.g. car, lorry etc.

In some embodiments the historic relative probability data is indicativeof the historic probability of each possible outgoing path having beentaken during one or more time periods and/or by one or more types ofvehicle. The relative probability that each outgoing path from thedecision point will be taken may then be determined using the historicprobability data for the time period appropriate for the time at whichthe horizon is generated and/or for the appropriate vehicle type.

The method may extend to obtaining the positional data. The step ofobtaining the positional data may comprise receiving the data fromdevices associated vehicles, or may comprise accessing stored positionaldata. The method may thus comprise obtaining positional data relating tothe movement of a plurality of devices associated with vehicles withrespect to time in a road network, and filtering the data to obtain datarelating to the travel of devices (and hence vehicles) along the or eachof the plurality of possible outgoing paths from the decision point inrespect of the given incoming path. The method may then comprise usingthe data to obtain the historic relative probability data. The data maybe used in determining a count of the number of times each possibleoutgoing path is taken, and determining a relative probability that eachoutgoing path was taken for the given incoming path.

In some embodiments the method comprises generating and/or providing aprobability matrix, the probability matrix comprising, in respect ofeach of one or more decision points of a road network, data indicativeof the relative probability that each of a plurality of possibleoutgoing paths at the decision point will be taken by a vehicle for eachof one or more possible incoming paths, wherein the data indicative ofthe relative probability that a given possible outgoing path will betaken is based upon historical data relating to the position of aplurality of devices associated with vehicles with respect to time.Preferably the matrix comprises data indicative of the relativeprobability of each possible outgoing path being taken at one or more,and preferably a plurality of, decision points for each possibleincoming path at the decision point. The method may comprise using sucha probability matrix in determining the relative probability data forthe different outgoing paths. The data of the probability matrix may betime dependent and/or vehicle type dependent, and thus may be based ondata relating to movements of devices associated with vehicles in agiven time period and/or relating to devices associated with vehicles ofa given type. Probability data may be determined for each of a pluralityof different time periods and/or vehicles types.

The method may comprise storing such a probability matrix.

The present invention extends to a data product comprising such aprobability matrix.

In accordance with a further aspect of the invention there is provided adata product comprising a probability matrix having, in respect of eachof one or more decision points of a road network, data indicative of therelative probability that each of a plurality of possible outgoing pathsat the decision point will be taken by a vehicle for each of one or morepossible incoming paths, wherein the data indicative of the relativeprobability that a given possible outgoing path will be taken is basedupon positional data relating to the movements of a plurality of devicesassociated with vehicles with respect to time.

The present invention in this further aspect may include any or all ofthe features described with reference to the other aspects of theinvention to the extent they are not mutually exclusive.

In accordance with the invention in any of its aspects or embodimentsinvolving a probability matrix, the step of providing the probabilitymatrix may comprise obtaining positional data relating to the positionof a plurality of devices associated with vehicles with respect to timein a road network, and filtering the positional data to obtain dataindicative of the travel of vehicles along each possible outgoing pathat the or each decision point of the road network, and with respect tothe or each incoming path at the or each decision point. The filtereddata may then be used to determine the relative probability that each ofthe plurality of paths at a decision point is taken. The data may befiltered according to time period and/or vehicle type.

In accordance with any embodiment using positional data, the method mayextend to the step of obtaining the positional data relating to themovement of devices associated with vehicles. The step of obtaining thepositional data may or may not comprise receiving the data from the oneor more devices. In some arrangements the step of obtaining the data maycomprise accessing the data, i.e. the data being previously received andstored. In arrangements in which the step of receiving the data involvesreceiving the data from the devices, it is envisaged that the method mayfurther comprise storing the received positional data before proceedingto carry out the other steps of the present invention, and optionallyfiltering the data. The step of receiving the positional data need nottake place at the same time or place as the other step or steps of themethod.

In embodiments the positional data is received at a central controller,such as a server system. The server may carry out the steps of using thepositional data to determine a relative probability that each of aplurality of paths will be taken, or to determine the probabilitymatrix.

The positional data used in accordance with the invention, at least inpreferred embodiments, is collected from one or more, and preferablymultiple devices, and relates to the movement of the devices withrespect to time. Thus, the devices are mobile devices. It will beappreciated that at least some of the positional data is associated withtemporal data, e.g. a timestamp. For the purposes of the presentinvention, however, it is not necessary that all positional data isassociated with temporal data, provided that it may be used to providethe data relating to the probability of different paths being taken inaccordance with the present invention. However, in preferred embodimentsall positional data is associated with temporal data, e.g. a timestamp.

The devices are associated with vehicles. The position of a device canbe assumed to correspond to the position of a vehicle. Thus referencesto positional data obtained from devices associated with vehicles, maybe replaced by a reference to positional data obtained from a vehicle,and references to the movement of a device or devices may be replaced bya reference to the movement of a vehicle, and vice versa, if notexplicitly mentioned. The device may be integrated with the vehicle,e.g. in-built sensor or navigation apparatus, or may be a separatedevice associated with the vehicle, such as a portable navigationapparatus. Of course, the positional data may be obtained from acombination of different devices, or a single type of device, e.g.devices associated with vehicles.

The devices may be any mobile devices that are capable of providing thepositional data and sufficient associated timing data for the purposesof the present invention. The device may be any device having positiondetermining capability. For example, the device may comprise means foraccessing and receiving information from WiFi access points or cellularcommunication networks, such as a GSM device, and using this informationto determine its location. In preferred embodiments, however, the devicecomprises a global navigation satellite systems (GNSS) receiver, such asa GPS receiver, for receiving satellite signals indication the positionof the receiver at a particular point in time, and which preferablyreceives updated position information at regular intervals. Such devicesmay include navigation devices, mobile telecommunications devices withpositioning capability, position sensors, etc.

The steps of the methods described herein in any of its embodiments forgenerating a horizon are preferably carried out by a horizon generatingsubsystem of an ADAS. The ADAS is associated with a vehicle. The horizongenerating subsystem may be provided by a suitable software module ormodules, for example. The horizon generating subsystem is preferably incommunication with one or more ADAS applications of a vehicle over avehicle communication network, e.g. CAN bus.

A horizon generating subsystem may comprise means for storing data usedin generating the horizon, or such data may be stored separately to thehorizon generating subsystem. Similarly horizon data, e.g. probabilitydata, may be stored by a memory of the horizon generating subsystem orelsewhere.

Preferably the data used in generating the horizon, or, wheredetermined, probability data is stored locally to the vehicle, e.g. on amemory of the ADAS.

The method may further comprise the step of using the determined horizondata, e.g. relative probability data for a plurality of outgoing pathsassociated with a decision point, to determine one or more predictedpaths along which the vehicle can be expected to travel in the immediatefuture, e.g. one or both of a most probable path and at least onealternative path. The at least one alternative path preferably comprisesat least a most probable alternative path. The method may comprisestoring data indicative of the or each determined path. The one or moreof the most probable outgoing path and the at least one alternative pathare preferably outgoing paths at the decision point.

The step of generating the horizon may comprise determining a mostprobable path the vehicle may be expected to travel in the immediatefuture, and at least one alternative path, wherein the positional datais used in determining the most probable path and/or the at least onealternative path.

In preferred embodiments in which relative probability data isdetermined for following each of a plurality of different possibleoutgoing paths at a decision point, the method may comprise determininga most probable outgoing path based on the probability data to betraveled by the vehicle from the decision point and/or determining therelative probabilities associated with one or more, and preferably aplurality of, alternative outgoing paths at the decision point using theprobability data.

The methods and systems of the present invention are applicable whetheror not the vehicle is following a pre-calculated route. In someembodiments the vehicle is a vehicle that is following a pre-calculatedroute, while in other embodiments the vehicle is a vehicle that is notfollowing a pre-calculated route. In the latter case, the vehicle willbe so-called “free driving”.

The most probable outgoing path based on the relative probability datamay be taken to be the most probable path to be traveled by the vehiclein embodiments in which the vehicle is not following a pre-calculatedroute.

As used herein, a “pre-calculated route” refers to a route that has beencalculated between an origin and a destination. The route may be a routethat has been pre-calculated by a navigation device associated with thevehicle. The navigation device may be an integrated or portablenavigation device. The pre-calculated route is, in these embodiments, aroute that has been calculated before the step of generating the ADAShorizon takes place. The method may further comprise the step ofcalculating a route that is to be followed by the vehicle between anorigin and a destination prior to the step of generating the ADAShorizon, and the system may comprise means for calculating a route. Theroute may be pre-calculated before the vehicle commences travel, or maybe a route that is calculated en-route, e.g. in the event of a deviationfrom an originally planned route. The method may comprise generating theADAS horizon during travel of the vehicle along the pre-calculatedroute.

When the vehicle is following a pre-calculated route, the most probablepath may be assumed to correspond to a portion of the pre-calculatedroute ahead. Thus, in embodiments in which the vehicle is following apre-calculated route, the most probable path, and hence in embodimentsthe most probable outgoing path at the decision point, is assumed tocorrespond to the pre-calculated route, or the outgoing pathcorresponding to a portion thereof. This may or may not be the same asthe most probable path indicated in preferred embodiments by therelative probabilities determined using the positional data. In someembodiments in which one of the possible outgoing paths is known tocorrespond to a portion of a pre-calculated route, the method maycomprise excluding that outgoing path from the set of plurality ofoutgoing paths whose relative probabilities are determined, or adjustingthe calculations appropriately to ensure that this route is determinedto be the most probable, as described above.

Where an outgoing path corresponds to a pre-calculated route, the methodof the present invention in its preferred embodiments may be used todetermine the relative probabilities that each of a plurality ofoutgoing paths other than that corresponding to the pre-calculated routewill be taken at the decision point. These paths will providealternative paths diverging from the pre-calculated route at thedecision point. The present invention may then provide the ability todetermine the relative probability that the vehicle will follow any ofthese alternative paths if the path of the vehicle diverges from themost probable path, i.e. that corresponding to the pre-calculated routeat the decision point.

Determining of an alternative path emanating from a decision point aswell as the most probable path is advantageous as the alternative pathmay be taken to be the most likely path to be taken if the vehiclediverges from the most probable (main) path. By including dataindicative of the probability that different alternative paths may betaken at the decision point in the horizon, the ADAS may be able tocontinue to operate, and obtain data relating to the path, if thevehicle deviates from the expected main path, reducing the likelihood of“blind driving”.

The method preferably comprises providing storing data indicative of thegenerated horizon and/or providing data indicative of the generatedhorizon over a vehicle bus to one or more ADAS applications of thevehicle (e.g. to the client side of the vehicle ADAS). Preferably thesesteps are carried out by a horizon generating subsystem of the ADAS.

The ADAS applications are for controlling respective subsystems of thevehicle. The one or more ADAS applications may be arranged forcontrolling one or more of: a braking function, the suspension, and thespeed selection subsystem of the vehicle.

In embodiments in which the step of generating the horizon comprisesdetermining one or more predicted paths, e.g. one or both of a mostprobable path and an alternative path, the method may comprise storingdata indicative of the or each path and/or providing, e.g. transmittingsuch data over a vehicle bus to one or more ADAS applications of thevehicle for use by the one or more applications.

In embodiments in which data indicative of one or more predicted path isstored and/or provided over the bus, the data may comprise one or moreattributes of the path, or data allowing such attributes to bedetermined. The attribute data may comprise, as needed, informationidentifying a location associated with the one or more attributes. Forexample, the attribute data may indicate the start and end of a portionof a road segment with high curvature.

Attribute data in respect of a path of a horizon refers to properties ofthe predicted path ahead of a vehicle's current position, and mayinclude any or all of: a gradient of a segment, a curvature of asegment, a height of a segment, geometry of a segment, and a speedprofile associated with the segment. Thus the attribute data may reflectinherent properties of the segment, or for example relate to expectedvehicle speed data along the segment. The attribute data may be anyattribute data that may be used by one or more ADAS application toimplement one or more ADAS function. Thus, in some embodiments, themethod may further comprise an ADAS application of the vehicle using theattribute data transmitted over the vehicle bus to carry out one or moreof: issuing a speed warning, providing a speed recommendation, andautomatically controlling the braking function of the vehicle.

Preferably such attribute data is provided at least in respect of adetermined most probable path, and in some instances only for the mostprobable path. In these latter embodiments preferably data indicatingthe presence and/or location of one or more alternative paths along themost probably path is provided over the bus. The data indicative of thepresence of the one or more alternative paths preferably comprises dataindicative of the relative probability that the path will be taken atthe decision point determined in accordance with the invention. Inpreferred embodiments relative probability data is provided for eachalternative path for which relative probability data has beendetermined. In these embodiments, the ADAS applications would use theindication of the presence of an alternative path to request furtherdata, e.g. attribute data, from the horizon generator when the vehicleis found to diverge from a most probable route.

It will be appreciated that the methods in accordance with the presentinvention may be implemented at least partially using software. It willthis be seen that, when viewed from further aspects, the presentinvention extends to a computer program product comprising computerreadable instructions adapted to carry out any or all of the methoddescribed herein when executed on suitable data processing means. Theinvention also extends to a computer software carrier comprising suchsoftware. Such a software carrier could be a physical (ornon-transitory) storage medium or could be a signal such as anelectronic signal over wires, an optical signal or a radio signal suchas to a satellite or the like.

The present invention in accordance with any of its further aspects orembodiments may include any of the features described in reference toother aspects or embodiments of the invention to the extent it is notmutually inconsistent therewith.

It should be noted that the phrase “associated therewith” in relation toone or more segments should not be interpreted to require any particularrestriction on data storage locations. The phrase only requires that thefeatures are identifiably related to a segment. Therefore associationmay for example be achieved by means of a reference to a side file,potentially located in a remote server.

Advantages of these embodiments are set out hereafter, and furtherdetails and features of each of these embodiments are defined in theaccompanying dependent claims and elsewhere in the following detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present invention will now be described, byway of example only, and with reference to the accompanying drawings inwhich:

FIG. 1 shows the components of an exemplary ADAS system associated witha vehicle, which may be used to implement the methods of the presentinvention;

FIG. 2 schematically illustrates the concept of an ADAS horizon;

FIG. 3A illustrates a portion of a road network ahead of a currentposition of a vehicle up to a distance in the direction of traveldefined by a limit of the distance that the ADAS horizon will extendonce generated;

FIG. 3B illustrates a number of paths which may be taken through thenetwork; and

FIG. 3C illustrates the way in which these paths may be represented toan ADAS application.

DETAILED DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic illustration of the components of an exemplaryADAS system associated with a vehicle, which may be used to implementthe methods of the present invention.

The ADAS system 1 includes a horizon generating subsystem 3, whichcommunicates horizon data over a Controller Area Network (CAN) bus 5 toa plurality of ADAS applications 7, 9 and 11. The ADAS applications arearranged to implement ADAS functionality in controlling respectivesubsystems of the vehicle. For example, the ADAS applications may be forcontrolling braking, speed selection and suspension vehicle subsystemsrespectively. In use, the respective ADAS applications filter therelevant data from the horizon data for use in controlling theirassociated vehicle subsystem.

The horizon providing subsystem 3 is arranged to generate dataindicative of a driving horizon which is used by the ADAS applications(the “ADAS horizon”). The ADAS horizon is an electronic horizoncomprising data indicative of one or more predicted paths ahead of acurrent position of a vehicle that it may be expected the vehicle willtravel along. The ADAS horizon is based on digital map data.

In order to provide ADAS functions, the ADAS applications requireinformation about the road ahead, and its attributes, e.g. gradient,curvature, speed limit, etc. The ADAS horizon provides this informationin respect of the one or more predicted paths up to a given distanceahead of the current position. The distance ahead may be 200 m. The ADAShorizon data that is transmitted over the vehicle bus 5 to the ADASapplications contains at least attribute data of the most probable path(up to a certain, often predetermined, extent ahead of the vehicle). TheADAS horizon generating subsystem 3 provides the attribute data inrelation to any attributes that may be required by the different ADASapplications 7, 9, 11, or data allowing such attribute data to berequested by the applications, and the respective subsystems may thenselect or request the attribute data relevant to their operation. Forexample, data relating to the curvature of the path ahead may beselected by an ADAS application for controlling braking of the vehicle.The attribute data may be provided in fields to facilitate filtering ofrelevant data by the ADAS applications.

The horizon generating subsystem 3 is arranged to determine a horizon inaccordance with any of the embodiments described herein, and to causethe horizon data to be transmitted over the vehicle bus to the ADASapplications. The horizon generating subsystem 3 may be arranged to bein communication with a memory for storing generated horizon data. Itwill be appreciated that as described below, not all horizon datadetermined is necessarily transmitted at a given time over the vehiclebus. For example, data relating to alternative paths rather than adetermined most probable path may be determined and stored, but nottransmitted over the bus unless required to avoid overloading the ADASapplications. The ADAS horizon generating subsystem is also incommunication with digital map data. This is used in determining thehorizon. In some arrangements the ADAS horizon generating subsystem maybe implemented using a software module separate from a digital map datastore, or may otherwise include means storing such data. Thus thedigital map data providing and horizon generating functions may beimplemented separately or as part of a combined system.

FIG. 2 schematically illustrates the concept of an ADAS horizon. It maybe seen that the electronic ADAS horizon 10 provides the ADAS withinformation about the path ahead in a similar manner to a vehicle sensorhorizon 12, but may provide information about the path beyond the limitof a vehicle sensor horizon, e.g. around a corner, and regardless ofweather conditions, as the ADAS horizon is based upon digital map data.

At various points in a road network there will be nodes, i.e. decisionpoints at which the vehicle may have a choice of possible outgoingpaths. The present invention relates, in embodiments at least, to amethod of more reliably determining the most probable path at a decisionpoint, and the relative probability that each of a plurality ofalternative paths may be taken. Some exemplary methods will be describedby reference to a decision point that is a junction.

At a given time, a vehicle has a current position defined on aparticular road segment. It may be assumed that the continuation of thesegment forms the most probable path until the first junction isreached. At the first junction the method of the present invention maybe used to determine the relative probability that each of the possibleoutgoing paths may be taken. This enables a determination to be made asto which of the plurality of outgoing paths forms the continuation ofthe main path, if this is not already known (e.g. from a pre-calculatedroute), and which form alternative paths. A determination is also madeas to the relative likelihood that one of these alternative paths may betaken.

In order to determine this, a probability is determined for eachoutgoing path at a decision point indicative of the likelihood that thepath will be chosen in preference of all other possible outgoing paths.In this process, certain outgoing paths which are considered not to bepossible outgoing paths may be excluded from the determination, e.g.they may be designated “restricted” paths. These may be excluded byassigning each a probability of “0”.

The way in which the probability for each outgoing path is determined isdependent upon whether the vehicle is following a pre-calculated route.

Where no route has been pre-calculated, a probability is determined foreach outgoing path at a decision point indicative of the likelihood thatthe path will be taken to the detriment of all other outgoing paths.This is done using the probability matrix described below. The mostprobable path may be determined as the most probable path continuingfrom the decision point. Each other outgoing path may then be classifiedas an alternative path.

Data is stored indicative of the identity and probability associatedwith the determined most probable path and each alternative path. Thisdata is associated with data indicative of the junction to which itrelates.

When a route is pre-calculated, it may be assumed that the most probableoutgoing path at the decision point is the outgoing path from thejunction which is along the pre-calculated route. This may override anyrestriction of the path. The remaining non-restricted outgoing paths aredetermined to be alternative paths.

Data is stored indicative of the identity and probability of eachalternative path associated with data indicative of the junction towhich this relates.

The probability that a given possible outgoing path from the junctionwill be taken is calculated based on a historical probability that thepath has been taken determined using vehicle probe data (collected overa relatively long period of time, e.g. weeks, months, etc), i.e.positional data with respect to time. Such data may be referred to as“historical probe data”. The vehicle probe data may be obtained from anysuitable devices associated with the vehicles, such as ADAS, navigationdevices, or any other suitable electronic device.

Vehicle data is used to build up a probability matrix in respect ofpaths being taken at each of a plurality of junctions in the roadnetwork. Probe data relating to the movement of vehicles in the networkis filtered to extract data relating to paths taken by vehicles passingthrough each junction. The probe data comprises a set of data including,for each vehicle, data indicative of a plurality of positions andassociated timestamps representing the movement of the vehicle, i.e. apositional or probe trace. The probe data relating to vehicles that havetraversed a given junction may readily be selected by reference todigital map data identifying the location of the junction. The data isused to determine a count of vehicles that have taken each possibleoutgoing path at the junction for each possible incoming path. This mayinclude U turn manoeuvres. The set of possible incoming paths and theset of possible outgoing paths at each junction may be represented byrespective incoming and outgoing vectors. For example, each positionaltrace may be assigned to a bin depending upon the incoming and outgoingpaths used. There may be a bin for each combination of an outgoing andincoming path at the junction. From this data, a probability matrixdefining a probability of each possible outgoing path being taken foreach possible incoming path at the junction is derived. The probabilitydata determined for a junction provides a probability matrix for thejunction that may be stored in a suitable database. The relevantprobability data is stored in the database in association with dataidentifying the junction to which it relates. A probability matrix maybe determined for each junction in the region.

When the vehicle approaches a given junction, the relevant data may beobtained from the database representing the (historic) probability thateach outgoing path may be taken based on the current incoming path. Inthis way, these probabilities for each outgoing path based on historicalprobe data may be used in determining the probability associated witheach outgoing path.

In preferred embodiments the probability matrix data relating to theprobability of each possible outgoing path at a given junction beingtaken for each possible given incoming path is time dependent. In theseembodiments, probability data may be determined and stored in respect ofeach of a plurality of different time periods. The time periods may betimes of day. This may be done by reference to probe data relating tomovements of vehicles only in the time period of interest. In thesepreferred embodiments, when a vehicle approaches a given junction, thehistoric probability data that is derived from the database is that forthe corresponding time of day. Alternatively or additionally, theprobability matrix data may be dependent upon vehicle type, e.g. lorry,car etc. The data relating to the type of vehicle that is approachingthe junction may then be used to determine the probability of thevehicle taking a given outgoing path.

In accordance with the invention, probabilities associated with a mostprobable path and multiple alternative outgoing paths emanating from thejunction are determined by the horizon generating subsystem. The horizongenerating subsystem stores data indicative of each path and itsprobability.

Once determined, the horizon generating subsystem may then provide dataindicative of each path and its associated probability over a vehiclebus to the one or more subsystems. There are various manners in whichthis may be done.

It is desirable to reduce the amount of horizon data transmitted overthe CAN bus. For this reason, in certain embodiments, only attributedata for the most probable path is transmitted over the vehicle bus,together with data identifying the location of any such attributes, e.g.relative to the current position of the vehicle. The most probable pathmay be referred to as the “main path”. This is the most probable futuretrajectory of the vehicle up to the limit of the ADAS horizon, asdetermined by the ADAS horizon providing subsystem 3.

At each decision point along the main path there will be a possiblealternative path that the vehicle may take if it diverges from the mainpath. An alternative path that emanates from a decision point along themain path may be referred to as a first level sub path beneath the mainpath. A path branching off from the first level sub path is referred toas a second level sub path and so on. This concept is illustrated byreference to FIGS. 3A, 3B and 3C.

FIG. 3A illustrates a portion of a road network ahead of a currentposition 20 of a vehicle up to a distance in the direction of traveldefined by a limit of the distance that the ADAS horizon will extendonce generated, e.g. 200 m. The road network is made up of a pluralityof links or road segments, e.g. 21, 22, connected by nodes e.g. 24. Thegeneration of the ADAS horizon considers possible paths, i.e.trajectories, that may be taken by the vehicle through the road networkrather than individual road segments and nodes.

FIG. 3B illustrates a number of paths which may be taken through thenetwork shown in FIG. 3A. Each of the paths has a probability that thedriver will follow it. This may be used to determine the most probableor main path that can be expected to be followed, and in many cases atleast a first level sub path. The first level sub path may be consideredto be an alternative path that may be taken at a given decision pointalong the main path.

FIG. 3C illustrates the way in which these paths may be represented toan ADAS application. This represents schematically the relationshipbetween the possible paths through the road network. Path 2 forms themost probable or main path in this case, and paths 1, 3 and 4 are firstlevel sub paths diverging from the main path at different respectivedecision points along its length. Path 5 is a second level sub pathsdiverging from the first level sub paths 4 at a decision point along itslength.

The ADAS horizon generating subsystem 3 will determine the mostprobable, i.e. main path 2. As discussed above, in some simple systems,the ADAS horizon generating subsystem could just transmit attribute datafor this main path over the vehicle bus. However, if the vehiclediverges from the main path, then the system will be left “blind” untila new most probable or main path is generated. Thus, it is beneficialfor the horizon generating subsystem 3 to also determine at least thefirst order sub paths diverging from the main path.

Where one or more first level sub paths are determined, the horizongenerating subsystem 3 may provide attribute data for the or each firstlevel sub path over the vehicle bus to the ADAS applications, togetherwith the corresponding data for the main or most probable path. However,to reduce the amount of data transmitted, in some arrangements onlyminimal data is transmitted regarding the presence of a first level subpath and its location along the main path.

When a most probable and one or more alternative paths at a decisionpoint is determined in accordance with the invention, the horizonprovider may represent the determined paths in any of these manners tothe ADAS applications. Each alternative outgoing path at the decisionpoint, e.g. junction may be represented as a first level sub pathemanating from the main or most probable path at the junction.

The data transmitted regarding the main path, and in some embodimentsone or more alternative paths, may include any of the following dataabout attributes of the road segment or segments making up thedetermined portion of the main path: speed limit, recommended speedlimit where no legal speed limit is associated with the road segment,functional road class, form of way, gradient, curvature, etc.

The data may be used by the ADAS applications as desired. In preferredembodiments the received horizon data is used to carry out at least oneof: providing an over-speed warning, adjusting a current speed, oroperating a braking subsystem of the vehicle. The ADAS applications maycontrol speed based on a curvature, gradient or speed limit associatedwith the most probable or main path.

Although the present invention has been described with reference topreferred embodiments, it will be understood by those skilled in the artthat various changes in form and detail may be made without departingfrom the scope of the invention as set forth in the accompanying claims.

The invention claimed is:
 1. A computer implemented method of generatingand using an electronic horizon for use with an advanced driverassistance system (ADAS) of a vehicle, comprising: accessing, by aprocessor, a probability matrix stored in a memory having, in respect ofeach of one or more decision points of a road network, data indicativeof a historic relative probability that each of a plurality of possibleoutgoing paths at the decision point will be taken by a vehicle for eachof one or more possible incoming paths, wherein the data indicative ofthe historic relative probability that a given possible outgoing pathwill be taken is based upon positional data relating to the movements ofa plurality of devices associated with vehicles with respect to time onthe road network; using, by the processor, the probability matrix todetermine data indicative of a relative probability that each of aplurality of possible outgoing paths associated with a decision pointwill be taken by the vehicle when confronted with the decision point;generating, by the processor, the electronic horizon from the determineddata, wherein the electronic horizon defines at least a most probableoutgoing path to be taken by the vehicle from the decision point; andproviding, by the processor, data to the ADAS of the vehicle determinedusing the electronic horizon to provide assistance to a driver of thevehicle.
 2. The method of claim 1, wherein the method comprisesassociating a relatively higher probability of the path being taken witha possible outgoing path that is associated with a relatively higherprobability of having been selected based on the historic relativeprobability data.
 3. The method of claim 1, wherein the historicrelative probability data is indicative of the historic probability ofeach possible outgoing path having been taken during one or more timeperiods, and wherein the relative probability that each outgoing pathfrom the decision point will be taken is determined using the historicprobability data for the time period appropriate for the time at whichthe electronic horizon is generated.
 4. The method of claim 1, whereinthe historic relative probability data is indicative of the historicprobability of each possible outgoing path having been taken by one ormore types of vehicle, and wherein the relative probability that eachoutgoing path from the decision point will be taken is determined usingthe historic probability data for the appropriate vehicle type.
 5. Themethod of claim 1, comprising obtaining the positional data relating tothe movement of a plurality of devices associated with vehicles withrespect to time in a road network, filtering the data to obtain datarelating to the travel of devices along the or each of the plurality ofpossible outgoing paths from the decision point in respect of a givenincoming path, and using the filtered data to determine the historicrelative probability data.
 6. The method of claim 1, comprisinggenerating the probability matrix and storing the probability matrix inthe memory.
 7. The method of claim 1, further comprising storing thedetermined data indicative of the relative probability that eachpossible outgoing path will be taken, in association with at least oneof: data indicative of the path to which the probability data relates;and the decision point to which it relates.
 8. The method of claim 1,wherein the step of generating the electronic horizon is carried out bya horizon generating subsystem of the ADAS associated with the vehicle,the method further comprising the horizon generating subsystem providingthe data determined using the generated electronic horizon over avehicle bus to one or more ADAS applications of the vehicle for use bythe one or more ADAS applications in controlling one or more vehiclesubsystems of the vehicle.
 9. The method of claim 8, wherein the one ormore ADAS applications use the data provided over the vehicle bus tocarry out one or more of: issuing a speed warning, providing a speedrecommendation, and automatically controlling the braking function ofthe vehicle.
 10. A non-transitory computer-readable medium comprisingcomputer readable instructions executable to perform a method ofgenerating and using an electronic horizon for use with an advanceddriver assistance system (ADAS) of a vehicle, the method executed by theset of instructions comprising: accessing, by a processor, a probabilitymatrix stored in a memory having, in respect of each of one or moredecision points of a road network, data indicative of a historicrelative probability that each of a plurality of possible outgoing pathsat the decision point will be taken by a vehicle for each of one or morepossible incoming paths, wherein the data indicative of the historicrelative probability that a given possible outgoing path will be takenis based upon positional data relating to the movements of a pluralityof devices associated with vehicles with respect to time on the roadnetwork; using, by the processor, the probability matrix to determinedata indicative of a relative probability that each of a plurality ofpossible outgoing paths associated with a decision point will be takenby the vehicle when confronted with the decision point; generating, bythe processor, the electronic horizon from the determined data, whereinthe electronic horizon defines at least a most probable outgoing path tobe taken by the vehicle from the decision point; and providing, by theprocessor, data to the ADAS of the vehicle determined using theelectronic horizon to provide assistance to a driver of the vehicle. 11.An apparatus for generating and using an electronic horizon for use withan advanced driver assistance system (ADAS) of a vehicle, comprising: amemory storing a probability matrix having, in respect of each of one ormore decision points of a road network, data indicative of a historicrelative probability that each of a plurality of possible outgoing pathsat the decision point will be taken by a vehicle for each of one or morepossible incoming paths, wherein the data indicative of the historicrelative probability that a given possible outgoing path will be takenis based upon positional data relating to the movements of a pluralityof devices associated with vehicles with respect to time on the roadnetwork; and at least one processor arranged to: use the probabilitymatrix to determine data indicative of a relative probability that eachof a plurality of possible outgoing paths associated with a decisionpoint will be taken by the vehicle when confronted with the decisionpoint; generate the electronic horizon from the determined data, whereinthe electronic horizon defines at least a most probable outgoing path tobe taken by the vehicle from the decision point; and provide data to theADAS of the vehicle determined using the electronic horizon to provideassistance to a driver of the vehicle.
 12. The apparatus of claim 11,wherein the apparatus is configured to provide the data determined usingthe generated electronic horizon over a vehicle bus to one or more ADASapplications of the vehicle for use by the one or more ADAS applicationsin controlling one or more vehicle subsystems of the vehicle.
 13. Thecomputer-readable medium of claim 10, wherein the method comprisesassociating a relatively higher probability of the path being taken witha possible outgoing path that is associated with a relatively higherprobability of having been selected based on the historic relativeprobability data.
 14. The computer-readable medium of claim 10, whereinthe historic relative probability data is indicative of the historicprobability of each possible outgoing path having been taken during oneor more time periods, and wherein the relative probability that eachoutgoing path from the decision point will be taken is determined usingthe historic probability data for the time period appropriate for thetime at which the electronic horizon is generated.
 15. Thecomputer-readable medium of claim 10, wherein the historic relativeprobability data is indicative of the historic probability of eachpossible outgoing path having been taken by one or more types ofvehicle, and wherein the relative probability that each outgoing pathfrom the decision point will be taken is determined using the historicprobability data for the appropriate vehicle type.
 16. Thecomputer-readable medium of claim 10, comprising obtaining thepositional data relating to the movement of a plurality of devicesassociated with vehicles with respect to time in a road network,filtering the data to obtain data relating to the travel of devicesalong the or each of the plurality of possible outgoing paths from thedecision point in respect of a given incoming path, and using thefiltered data to determine the historic relative probability data. 17.The computer-readable medium of claim 10, comprising generating theprobability matrix and storing the probability matrix in the memory. 18.The computer-readable medium of claim 10, further comprising storing thedetermined data indicative of the relative probability that eachpossible outgoing path will be taken, in association with at least oneof: data indicative of the path to which the probability data relates;and the decision point to which it relates.
 19. The computer-readablemedium of claim 10, wherein generating the electronic horizon is carriedout by a horizon generating subsystem of the ADAS associated with thevehicle, the method further comprising the horizon generating subsystemproviding the data determined using the generated electronic horizonover a vehicle bus to one or more ADAS applications of the vehicle foruse by the one or more ADAS applications in controlling one or morevehicle subsystems of the vehicle.
 20. The method of claim 1, whereinthe step of generating the electronic horizon comprises using thedetermined data to predict the most probable outgoing path and at leastone alternative outgoing path that the vehicle may be expected to travelin the immediate future at the decision point.